In the installed packages section it includes

pywin32 == 303

even though that is not in my requirements.txt.

So for some reason it is being detected (meaning your code base actually imports it in code)
But you can just remove it, either by manually editing the cloned Task (right click, reset, then you can edit the section), or via code
Task.ignore_requirements("pywin32") task = Task.init(...)

JitteryCoyote63 while it's running, could you give me a few details on the setup, maybe I can reproduce it.
Is it using pytorch distributed ?
Are all models uploaded to S3 ?
etc.

Hi @<1523704207914307584:profile|ObedientToad56>

hat would be the right way to extend this with let's say a custom engine that is currently not supported ?

as you said 'custom' 🙂
None
This is actually a custom engine, (see (3) in the readme, and the preprocessing.py implementing it) I think we should actually add a specific example to custom so this is more visible. Any thoughts on what would...

It just seems frozen at the place where it should be spinning up the tasks within the pipeline

And is there an agent for those ? usually there is one agent for running logic tasks (like pipelines) running with --services-mode which means multiple Tasks can be executed by the same agent. And other agents for compute Tasks that are a signle Task per agent (but you can run multiple agents on the same machine)

IntriguedRat44 how do I reproduce it ?
Can you confirm that marking out the Task.init(..) call will fix it ?

JitteryCoyote63 The release was delayed due a last minute issue, should be released later today. Anyhow the code is updated on GitHub, so you can start implementing :) let me know if I can be of help :)

How do I reproduce it ? (all the processes are on the same machine?)

YEY! 🚀 🎉

That is a bit odd, But SSH keys have to have a specific chmod flags for them to work (security issues)
What was the error ?

Still not supported 😞

Thanks LethalCentipede31 , i think (3) is the most stable solution (as it doesn't require to add another package, and should work on any python version / OS)
This is actually what we do for downloads .
DO you know if there is a minimum required python requests version ?

Good point!
I'll make sure we do 🙂

Hi MelancholyBeetle72
You mean the venv creation takes the bulk of the time, or it something else ?

SourOx12
Hmmm. So if last iteration was 75, the next iteration (after we continue) will be 150 ?

Which means there will be atleast multiple published models entries of same model over time?

Only the specific one will be published (not all the Models the Task created)

im not running in docker mode though

hmmm that might be the first issue. it cannot skip venv creation, it can however use a pre-existing venv (but it will change it every time it installs a missing package)
so setting CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1 in non docker mode has no affect

if I encounter the need for that, I will adapt and open a PR 

Great!

Hi RoughTiger69
I'm actually not sure about DVC support as well, see in these links, syncing and registering is a link, not creating an immutable copy.
And the sync between the local and remote seems like it is downloading the remote and comparing to the local copy.
Basically adding remote source Does not mean DVC will create an immutable copy of the content, it's just a pointer to a bucket (feel free to correct me if I misunderstood their capability)
https://dvc.org/doc/command-reference/...

This is awesome man !

Could you verify the Task.init call is inside the main function and Not the global scope? We have noticed some issues with global scope calls in some cases

Sorry, env file for conda, the one you are using to install

Hi @<1529633468214939648:profile|CostlyElephant1>

Is it possible to get user ID of the current user

On the Task.data object itself there should be a filed named " user " that's the user ID of the owner (creator) of the Task.
You can filter based on this id with

Tasks.get_tasks(..., task_filter={'user': ["user-id-here"]})

wdyt?

Maybe the only thing to worry about is making sure the IP address is stable, so if k8s replaces the node, you do not have to reconfigure the clients 🙂

That sounds like success!

Hi WickedGoat98 ,
I think you are correct 😞
I would guess it is something with the ingress configuration (i.e. ConfigMap)

try these values:

os.environ.update({
    'CLEARML_VCS_COMMIT_ID': '<commit_id>',
    'CLEARML_VCS_BRANCH': 'origin/master',
    'CLEARML_VCS_DIFF': '',
    'CLEARML_VCS_STATUS': '',
    'CLEARML_VCS_ROOT': '.',
    'CLEARML_VCS_REPO_URL': '

',
})

task = Task.init(...)

PlainSquid19 yes the link is available on in the actual paid product 😞
I don't think they have the documentation open yet...
My recommendation is to fill the contact us form, you'll get a free online tour as well 😉

So this is optuna 🙂 the idea is it will test which parameters have potential (with early stopping), then launch a subset of the selected parameters

Correct 🙂
You can spin it in two modes, either venv or docker (notice that even in docker mode, it will still clone the code into the docker and install the packages inside the docker, but it also inherits from the docker preinstalled system packages, so that the installation process is a lot faster, but you have the ability to change packages without having to build an entire new docker image)

Basically it solves the remote-execution problem, so you can scale to multiple machines relatively easy :)